Optimization Algorithm of UAVs Task Assignment and Path Planning Based on Dynamic Cluster Particle Swarm Optimization

Task assignment and path planning are crucial links in the task execution of uncrewed aerial vehicle (UAV) cluster, especially in high-dimensional complex scenarios, the calculation difficulty increases significantly. To solve this problem, swarm intelligence as an efficient strategy emerged. In order to solve the challenge of incomplete information in the task assignment of UAVs and the problems of intra-group cooperation and competition, we propose an innovative multi-agent near-end strategy optimization algorithm (MAPPO algorithm). The algorithm is designed for the task assignment of UAV in incomplete information environment. By constructing a practical algorithm model and combining the incomplete information game theory, the original algorithm is optimized to better deal with the cooperation and competition mechanism within the UAVs. Secondly, the global search capability is poor and local optimization is easy to occur. The dynamic cluster particle swarm optimization (DCPSO) algorithm is proposed to model the task scenario of UAVs path planning problem by using artificial potential field method and rolling time domain control principle. Tent chaos mapping and dynamic cluster mechanism are introduced to further improve the global search capability and search accuracy. Finally, DCPSO algorithm is used to optimize the objective function of the model, and the selection of UAV trajectory points is obtained. Simulation results under different combinations of single-peak/multi-peak, low-dimensional/high-dimensional benchmark test functions show that DCPSO algorithm has better optimization ability, mean value and variance compared with PSO, pigeon inspired optimization (PIO), Sparrow search algorithm (SSA) and chaotic disturbed pigeon flock optimization (CDPIO) algorithms. Better search accuracy and stability.